大语言模型压缩综述OA

Large language models(LLMs)have attracted considerable attention in recent years due to their strong cognitive capabilities and widespread applications in various fields.However,their tremendous demand for computation and memory makes it difficult to deploy them in resource-constrained scenarios.Model compression and acceleration techniques have thus emerged as critical approaches to reduce computational complexity and memory usage while maintaining model performance.This paper presents a comprehensive survey of recent advances in LLM compression and acceleration methods,aiming to grasp the current development status and future trends of the entire field,promote the advancement of LLM compression and acceleration technologies,and facilitate their application and implementation in both industry and academia.It begins by outlining the challenges LLMs face in terms of computational and storage overhead.Then,it categorizes and reviews the main technical approaches,including model pruning,quantization,knowledge distillation,and low-rank decomposition,highlighting their core principles,representative methods,and cutting-edge developments.In addition,the paper provides a detailed discussion on evaluation metrics such as inference latency,accuracy retention,and deployment cost,establishing a multidimensional evaluation framework.Finally,it explores the promising future directions of LLM compression methods,aiming to guide future research and industrial deployment of compressed LLMs.